Class/Object

ksb.csle.didentification.privacy

PartialAggregateOperator

Related Docs: object PartialAggregateOperator | package privacy

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class PartialAggregateOperator extends BasePrivacyAnonymizer

:: ApplicationDeveloperApi ::

Operator that implements the partial aggregation module in the Aggregation algorithm. It discriminates outliers (currently, boxplot, z-score methods are supported) and then only replaces them with statistic information (e.x., min, max, avg, std, and count).

Linear Supertypes
BasePrivacyAnonymizer, DataFrameCheck, BaseDataOperator[StreamOperatorInfo, DataFrame], BaseGenericOperator[StreamOperatorInfo, DataFrame], BaseGenericMutantOperator[StreamOperatorInfo, DataFrame, DataFrame], BaseDoer, Logging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. PartialAggregateOperator
  2. BasePrivacyAnonymizer
  3. DataFrameCheck
  4. BaseDataOperator
  5. BaseGenericOperator
  6. BaseGenericMutantOperator
  7. BaseDoer
  8. Logging
  9. Serializable
  10. Serializable
  11. AnyRef
  12. Any
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Visibility
  1. Public
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Instance Constructors

  1. new PartialAggregateOperator(o: StreamOperatorInfo)

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    o

    Object that contains message ksb.csle.common.proto.StreamDidentProto.PartialAggrInfo PartialAggrInfo contains attributes as follows:

    • selectedColumnId: Column ID to apply the partial-aggregation function
    • method: the method to aggregate the given columns
    • outlierMethod: the method to discriminate the outlier
    • fieldInfo: the info about column attributes (identifier, sensitive, ..)
    • check: the method how to verify the performance of anonymized data

    PartialAggrInfo

    enum AggregationMethod {
      MIN = 0;
      AVG = 1;
      MAX = 2;
      STD = 3;
      COUNT = 4;
      MANUAL = 5;
    }
    enum OutlierMethod {
      ZSCORE = 0;
      BOXPLOT = 1;
    }
    message PartialAggrInfo {
      repeated int32 selectedColumnId = 1;
      required AggregationMethod method = 2 [default = AVG];
      required OutlierMethod outlierMethod = 3 [default = ZSCORE];
      repeated FieldInfo fieldInfo = 4;
      optional PrivacyCheckInfo check = 5;
    }

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def anonymize(src: DataFrame, columnNames: Array[String]): DataFrame

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    Definition Classes
    BasePrivacyAnonymizer
  5. def anonymize(src: DataFrame, columnName: String): DataFrame

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    Anonymizes the column specified in src dataframe using generic 'Type' method.

    Anonymizes the column specified in src dataframe using generic 'Type' method. The 'Type' is decided by inherited object module.

    src

    Dataframe to anonymize

    columnName

    Column to be anonymized

    returns

    DataFrame The dataframe which replaces original column with anonymized column

    Definition Classes
    BasePrivacyAnonymizer
  6. def anonymizeColumn(src: DataFrame, columnName: String): DataFrame

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    Discriminates outliers among the records in the column of src dataframe and then replaces them with stat.

    Discriminates outliers among the records in the column of src dataframe and then replaces them with stat. info.

    src

    Dataframe to encrypt

    columnName

    Column to be encrypted

    returns

    DataFrame Anonymized dataframe

    Definition Classes
    PartialAggregateOperatorBasePrivacyAnonymizer
  7. def anonymizeNumericColumn(src: DataFrame, columnName: String, aggrType: AggregationMethod, outlierType: OutlierMethod): DataFrame

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    Discriminates outliers in a column containing numerical data and then only replaces them with stat.

    Discriminates outliers in a column containing numerical data and then only replaces them with stat. info.

    src

    Dataframe to encrypt

    columnName

    Column to be encrypted

    aggrType

    Methods of statistic, e.x., min, max, avg, std, and count

    outlierType

    outlier method such as z-score and boxplot

    returns

    DataFrame Anonymized dataframe

  8. def anonymizeStringColumn(src: DataFrame, columnName: String, outlierType: OutlierMethod): DataFrame

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    Discriminates outliers in a column containing text data and then replaces them with the string with maximum frequency.

    Discriminates outliers in a column containing text data and then replaces them with the string with maximum frequency.

    src

    Dataframe to encrypt

    columnName

    Column to be encrypted

    outlierType

    outlier method such as z-score and boxplot

    returns

    DataFrame Anonymized dataframe

  9. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  10. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  15. def getColumnName(src: DataFrame, columnId: Int): String

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    Returns column name from src dataframe specified by the column ID defined by protobuf.

    Returns column name from src dataframe specified by the column ID defined by protobuf.

    src

    dataframe to get names of columns.

    columnId

    column ID to anonymize.

    returns

    String.

    Definition Classes
    DataFrameCheck
  16. def getColumnNames(src: DataFrame, columnIDs: Array[Int]): Array[String]

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    Returns column names from src dataframe specified by column IDs.

    Returns column names from src dataframe specified by column IDs. Note that the column with invalid IDs are ignored.

    src

    dataframe to get names of columns.

    returns

    Array[String].

    Definition Classes
    DataFrameCheck
  17. def getQuasiColumnIDs(fieldInfos: Array[FieldInfo]): Array[Int]

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    Definition Classes
    DataFrameCheck
  18. def getSensColumnIDs(fieldInfos: Array[FieldInfo]): Array[Int]

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    Definition Classes
    DataFrameCheck
  19. def getValidColumnIDs(src: DataFrame, columnIDs: Array[Int]): Array[Int]

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    Definition Classes
    DataFrameCheck
  20. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  21. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  22. def isValidColumnID(src: DataFrame, columnID: Int): Boolean

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    Checks the given column ID is valid.

    Checks the given column ID is valid.

    src

    dataframe to get names of columns.

    returns

    Boolean.

    Definition Classes
    DataFrameCheck
  23. def isValidColumnName(src: DataFrame, columnName: String): Boolean

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    Checks the given column Name is valid.

    Checks the given column Name is valid.

    src

    dataframe to get names of columns.

    columnName

    column Name.

    returns

    Boolean.

    Definition Classes
    DataFrameCheck
  24. val logger: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  25. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  26. final def notify(): Unit

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    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  28. def operate(df: DataFrame): DataFrame

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    Operates partial aggregation module for basic de-identification

    Operates partial aggregation module for basic de-identification

    df

    Input dataframe

    returns

    DataFrame Anonymized dataframe

    Definition Classes
    PartialAggregateOperator → BaseGenericOperator → BaseGenericMutantOperator
  29. val p: PartialAggrInfo

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  30. val privacy: PrivacyCheckInfo

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    Definition Classes
    BasePrivacyAnonymizer
  31. def stop: Unit

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    Definition Classes
    BaseGenericOperator → BaseGenericMutantOperator
  32. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  33. def toString(): String

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    Definition Classes
    AnyRef → Any
  34. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from BasePrivacyAnonymizer

Inherited from DataFrameCheck

Inherited from BaseDataOperator[StreamOperatorInfo, DataFrame]

Inherited from BaseGenericOperator[StreamOperatorInfo, DataFrame]

Inherited from BaseGenericMutantOperator[StreamOperatorInfo, DataFrame, DataFrame]

Inherited from BaseDoer

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped